Various systems and methods are well known for maximizing subsurface secondary and/or tertiary oil recovery. Current systems for maximizing secondary and/or tertiary recovery generally rely on many steps, in different systems, and software tools, which users need to setup and manage by themselves. This is a manual process, where the user will create a numerical analysis model of the reservoir, run the model with a few different operating decisions and/or parameters, analyze the results and choose the best answer. The unautomated process often requires running multiple applications, which are not integrated, to obtain results to be integrated. As a result of the different applications required, a significant amount of reformatting data between applications may be necessary, creating further labor and the potential for error. Moreover, as the process is manually performed in numerous locations, there is no electronic audit trail for later review. This may be further complicated as analysis tools are generally generic and not designed to integrate data and to provide and assess simulations according to varying criteria. Current systems provide very little feedback as to the quality of the model and checking to make sure that the results are realistic. They do not provide interactive graphical feedback to the user at various levels of field operations and they do not provide true optimization and decision support tools. They also do not leverage the true value of real time data from the field. As a result, current systems are manual, labor intensive, and require transfer of data from one system to another while requiring the users to verify that the output from one system is usable as the input to another system. These deficiencies in current systems mean that the number of people who can do this type of work is quite limited. As a result, this process is performed by a limited number of experts within an organization. With a currently available set of tools, even these experts take a very long time to perform the process and are prone to errors because of the manual nature of the process.
As a result of the limitations of current systems, users generally do not look at multiple scenarios to take into account possible uncertainties in the underlying numerical reservoir model. Nor do users exhaustively utilize optimization technologies to analyze, rank and choose the best development operations to increase secondary and/or tertiary oil recovery. This often precludes users from addressing uncertainties in a reservoir model by periodically reassessing selected scenarios based on data such as historical performance of the reservoir, patterns, wells, and/or zones or other data. Moreover, in addition to all the limitations listed above, current systems do not provide good tools to allow a user to update a model, or series of models. These difficulties in generating a first model serve as a deterrent to generation of later updates.
Nor do current systems address the overall performance of the field or effectiveness of secondary or tertiary recovery processes. Practitioners of the current processes will generally recognize that sweep efficiency is an important metric of recovery process effectiveness. Sweep efficiency can be calculated at different locations in a field and at different scales. For example, sweep efficiency could be calculated locally near a well, at a zone level, between two wells, at a pattern level, at a field level and at different levels in between. Currently, there is no good method to measure or calculate sweep efficiency health indicators. There is also no integrated system and method for simultaneous simulation and optimization of well production at different scales or ranks from the field to equipment levels.